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Retrieval-augmented generation provides contextual, up to date data to make LLMs useful.

Retrieval-augmented generation is a technique that addresses the limitations of LLMs by providing them with access to contextual, up-to-date data. RAG implementations, sometimes referred to as RAG models or patterns, work by combining a pre-trained LLM with a retrieval system of readily accessible information. The retrieval system is responsible for finding relevant information from a knowledge library, such as a database. The RAG models enable the LLM, or foundation model, to generate a more accurate answer with up-to-date and relevant context to the task at hand.

RAG models have been shown to be effective for a variety of knowledge intensive tasks, including:

RAG can also be used to allow a generative AI powered application to observe some background state, and tailor its generations accordingly. An example would be the ability to write code based on the code that a user is writing. Other examples include:

Retrieval-augmented generation is also useful for data that can’t be incorporated as training data

Based on the above, the most suitable use cases of RAG include:

Why retrieval-augmented generation? What are the alternatives to RAG when building Generative AI applications.

There are a number of alternatives to RAG when building generative AI applications. Some of the most popular alternatives include:

Why retrieval-augmented generation over fine tuning an LLM?

The core building blocks of a basic RAG architecture.

A basic retrieval-augmented generation architecture consists of three main components:

The following diagram shows a basic RAG architecture with the same retail example as before:

As a workaround for this lack on domain-specific context, retrieval-augmented generation is performed as follows:

Keeping RAG simple with minimal complexity, yet sophisticated to perform reliably at scale.

To achieve a performant, yet minimally complex RAG architecture, it starts with choosing the right systems. When choosing the systems, or technologies, for a RAG implementation, it is important to choose systems, or a system, that can achieve the following:

Options range from single purpose vector databases, to document and relational databases with native vector capabilities, and data warehouses and lakehouses. However, single purpose vector databases will immediately add sprawl and complexity. Data warehouses and lakehouses are inherently designed for long-running analytical type queries on historic data as opposed to the high volume, low latency and fresh data requirements of the GenAI apps that RAG powers. Additionally, relational databases bring rigid schemas that limit flexibility of adding new data and app requirements easily. That leaves document databases with native, or built-in, vector capabilities. In particular, MongoDB is built on the flexible document model and has native vector search, making it a vector database for RAG in addition to the industry leading database for any modern application.

Taking the power of LLMs to the next level with additional capabilities in your RAG implementation.

In addition to the core components, there are a number of additional capabilities that can be added to a RAG implementation to take the power of LLMs to the next level. Some of these additional capabilities include:

Ensuring your Generative AI-powered application will be secure, performant, reliable, scalable, when it goes global.

There are a number of things that can be done to ensure that a GenAI-powered application built with a RAG is secure, performant, reliable, and scalable when it goes global. Some of these things include:

By following these tips, it is possible to build GenAI-powered applications with RAG architectures that are secure, performant, reliable, and scalable.

, MongoDB’s leading developer data platform provides teams with a vector database that enables building sophisticated, performant RAG architectures that can perform at scale. All this while maintaining the highest levels of security and cloud agnosticism, and most importantly, without adding complexity and unnecessary costs.

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What is retrieval-augmented generation (RAG)?

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